New toolkit helps nurses use genomics in patient care

By Kiara Palmer
Assistant Public Affairs Specialist, NHGRI

Nurses and other health professionals looking to integrate genomics into patient care now have access to an online toolkit with more than 100 resources, part of a new website launched by the National Human Genome Research Institute.

Developed with input from clinical educators and administrators, The Method for Introducing a New Competency in Genomics (MINC) website provides resources for nursing leaders at all levels of genomics competency, ranging from basic knowledge about genomics to its practical impact on healthcare systems and policies.

The website addresses the need for healthcare professionals to stay abreast with the rapidly changing healthcare environment. Its resources can help practicing nurses care for patients undergoing genomic testing and treatments, build awareness in their communities, and understand how to prepare their workforce for emerging clinical applications.

“The MINC toolkit is a starting point for healthcare providers who want to promote genomic integration into practice to benefit their patients,” said Laura Lyman Rodriguez, Ph.D., director of the Division of Policy, Communication and Education at NHGRI. “It was designed based on the efforts of Magnet® hospital nurses whose experiences were used in the design and foundation for the toolkit.”

The toolkit is structured in a question and answer format, allowing users to tailor their interventions based on the resources that will work best for them in their unique clinical setting. A key feature of the toolkit is “Champion Stories”. These video testimonials from health administrators and educators describe how they overcame barriers as they developed the necessary genomics knowledge to offer personalized care to their patients.

GEDmatch Genesis

GEDmatch Genesis is a peek at things to come for GEDmatch. It provides two things:

Ability to accept uploads from testing companies with formats and SNP sets not compatible with the current main GEDmatch database.

A new comparison algorithm that we believe will provide better accuracy, and more flexibility. More info: The Genesis Algorithm

During this initial deployment, the GEDmatch Genesis database will be separate from the main GEDmatch database, and comparisons for one will not show entries made in the other. Eventually, the 2 databases will be merged, and results will include entries from both. Likewise, the benefits of the Genesis comparison algorithm will eventually become available to all GEDmatch users.

The initial offering of Genesis applications will be limited to autosomal DNA matches. That too will be expanded as we move forward in our effort to convert existing GEDmatch software to the new algorithm.

We hope you find this transition to GEDmatch Genesis useful.

The Genesis Algorithm

For several years, GEDmatch has provided genetic genealogists, both beginners and experts, the ability to search for matches among kits in their database without regard to vendor. Also, GEDmatch has provided a rich suite of analysis programs allowing users to dig deeply into the genetic details of their matches, enhance the reports from their vendors, and even pursue their own original research ideas. Our algorithms are evolving to extract the most trustworthy and meaningful matching information possible using the markers common to pairs of kits even though sometimes limited.

Unfortunately, all too often, kits appear to share a DNA segment purely by chance. To combat this confusing phenomenon, we recently have developed a reliability measure that allows users to assess the quality of a matching segment in an intuitively appealing fashion. We also use the measure to guide our matching algorithms as they wring the greatest amount of useful information possible from the markers common to pairs of kits.

If we could assume that marker characteristics were uniform in all regions within chromosomes, we could use a “one size fits all” requirement for matching segments as is sometimes done. Unfortunately, the relevant characteristics vary widely. Some long segments with few markers may be accidental matches. Some marker rich short segments are often discarded although they are profoundly non-random.

Using the characteristics of each and every marker in a segment, we compute the expected number of purely chance matches to it to be found in the database. That number is then used to classify the segment into one of several levels reflecting the likelihood that the random matches may overwhelm the real ones. When a user executes a one-to-many search or a one-to-one comparison specifying a minimum segment length, the display can then include an estimate of validity for each segment found.

One can assume those segments designated to be valid are the result of a DNA inheritance process rather than mere chance. Questions may still remain about how far back shared DNA originates, but a confounding factor has been removed.